Enhancing Path Planning Performance through Image Representation Learning of High-Dimensional Configuration Spaces

This paper presents a novel method for accelerating path-planning tasks in unknown scenes with obstacles by utilizing Wasserstein Generative Adversarial Networks (WGANs) with Gradient Penalty (GP) to approximate the distribution of waypoints for a collision-free path using the Rapidly-exploring Random Tree algorithm. Our approach involves conditioning the WGAN-GP with a forward diffusion process in a continuous latent space to handle multimodal datasets effectively. We also propose encoding the waypoints of a collision-free path as a matrix, where the multidimensional ordering of the waypoints is naturally preserved. This method not only improves model learning but also enhances training convergence. Furthermore, we propose a method to assess whether the trained model fails to accurately capture the true waypoints. In such cases, we revert to uniform sampling to ensure the algorithm's probabilistic completeness; a process that traditionally involves manually determining an optimal ratio for each scenario in other machine learning-based methods. Our experiments demonstrate promising results in accelerating path-planning tasks under critical time constraints. The source code is openly available at https://bitbucket.org/joro3001/imagewgangpplanning/src/master/.

Visualizing High-Dimensional Configuration Spaces For Robots: A Comprehensive Approach for Quantitative and Qualitative Analysis

The reconstruction of Configuration Space (CS) from a limited number of samples plays a vital role in expediting motion planning for random tree algorithms. Traditionally, the evaluation of CS reconstruction is performed through collision checking. However, employing the collision checker as an evaluation measure can be misleading. In particular, a collision checker may exhibit high accuracy even when only a subset of the original CS is reconstructed, limiting the motion planner's ability to find paths comparable to those in the original CS. Additionally, a significant challenge arises when dealing with high-dimensional CSs, as it becomes increasingly difficult, if not impossible, to perform qualitative evaluations when working in dimensions higher than three. In this paper, we introduce a novel approach for representing high-dimensional CSs of manipulator robots in a 2D format. Specifically, we leverage the kinematic chain of manipulator robots and the human ability to perceive colors based on hue. This allows us to construct a visualization comprising a series of pairs of 2D projections. We showcase the efficacy of our method in representing a 7-degree-of-freedom CS of a manipulator robot in a 2D projection. This representation provides qualitative insights into the joint boundaries of the robot and the collision state combinations. From a quantitative perspective, we show that the proposed representation not only captures accuracy but also furnishes additional information, enhancing our ability to compare two different high-dimensional CSs during the deployment phase, beyond what is usually offered by the collision checker. The source code is publicly available on our repository.

Improving Path Planning Performance through Multimodal Generative Models with Local Critics

This paper presents a novel method for accelerating path planning tasks in unknown scenes with obstacles by utilizing Wasserstein Generative Adversarial Networks (WGANs) with Gradient Penalty (GP) to approximate the distribution of the free conditioned configuration space. Our proposed approach involves conditioning the WGAN-GP with a Variational Auto-Encoder in a continuous latent space to handle multimodal datasets. However, training a Variational Auto-Encoder with WGAN-GP can be challenging for image-to-configuration-space problems, as the Kullback-Leibler loss function often converges to a random distribution. To overcome this issue, we simplify the configuration space as a set of Gaussian distributions and divide the dataset into several local models. This enables us to not only learn the model but also speed up its convergence. We evaluate the reconstructed configuration space using the homology rank of manifolds for datasets with the geometry score. Furthermore, we propose a novel transformation of the robot's configuration space that enables us to measure how well collision-free regions are reconstructed, which could be used with other rank of homology metrics. Our experiments show promising results for accelerating path planning tasks in unknown scenes while generating quasi-optimal paths with our WGAN-GP. The source code is openly available.